Method and system for deploying communication and sensing integrated base station

Abstract

The invention relates to the field of edge networks, in particular to a method and a system for deploying a communication and sensing integrated base station. The method comprises the following steps: carrying out system modeling to obtain a base station deployment model; evaluating the positioning performance of the base station deployment model according to the base station deployment model, and setting a target function and constraint with the maximization of the total rate as a target according to the positioning performance of the base station deployment model; through approximate convex relaxation, introducing an auxiliary vector and a semi-relaxation technology to process the target function and the constraint to obtain a convex target function and a convex constraint; and solving the convex objective function and the constraint to complete base station deployment. The method comprises the following steps of: setting a target function and a constraint which take maximization of a total rate as a target; then, through approximate convex relaxation, introducing an auxiliary vector and a semi-relaxation technology for processing to obtain a convex objective function and constraints; and a non-convex problem which is relatively difficult to solve is converted into a problem which can be directly solved, so that a base station deployment scheme meeting the maximum total rate is obtained, and deployment is completed.

Description

Technical Field

[0001] The present invention relates to the field of base station deployment, and more specifically, to a synaesthesia integrated base station deployment method and system. Background Art

[0002] As 5G commercialization matures and related standards are gradually frozen, its network performance and technical system are gradually stabilizing. Soon after, the concept of 6G was proposed, opening a new chapter in the future development of communication technology. 6G aims to meet the more stringent requirements of the social vision around 2030, which not only means that the three typical scenarios of 5G need to be enhanced, but also new scenarios need to be expanded to meet the needs of emerging services. Among them, ISAC, as a new 6G scenario proposed by the International Telecommunication Union (ITU), has shown vigorous development potential [1]. ISAC technology enables communication and perception functions to be realized simultaneously in the same system by sharing hardware resources, which has broad application prospects in many fields. Therefore, researching and developing a deployment strategy for ISAC base stations that can take into account both communication and perception functions is of great significance to support the development of future 6G networks. At present, the main problem with the deployment strategy of ISAC base stations is that the communication rate of the deployed base stations is insufficient.

[0003] The prior art discloses a base station deployment method and device for high-precision positioning terminals. The method combines multiple positioning methods such as AOA and TOA with environmental perception in synaesthesia, and simultaneously locates and builds maps to achieve multi-dimensional massive data collection, and realizes: more, that is, multiple mobile points of the second device cooperate with multiple first devices, through beam scanning, using AOA, TOA and other positioning technologies to obtain more data samples; accurate, that is, environmental perception, RSRP / RSRPP, etc.; fast, that is, using edge link communication technology to replace the original base station anchor point with the user equipment as the basis for sampling points, so as to achieve rapid site location planning. However, the base station deployed by this method is still insufficient in communication rate. Summary of the invention

[0004] The purpose of the present invention is to disclose a method and system for deploying a synaesthesia-integrated base station with better base station communication rate.

[0005] In order to achieve the above object, the present invention provides a method for deploying a base station with integrated communication, comprising:

[0006] S1: Perform system modeling on base station deployment to obtain a base station deployment model;

[0007] S2: According to the base station deployment model, the positioning performance of the base station deployment model is evaluated, and the objective function and constraints with the goal of maximizing the total rate are set according to the positioning performance of the base station deployment model;

[0008] S3: The objective function and constraints are processed by approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain convex objective function and constraints;

[0009] S4: Solve the convex objective function and constraints to complete the base station deployment.

[0010] Further, in step S1, the base station deployment model includes: J candidate sites as candidate locations for base station deployment, and the states x of all candidate sites are stored by deploying decision vectors j ∈{0,1}, the coordinates of the base station are denoted by q i =(x i ,y i ); the coordinates of the test point a are denoted as p = (x, y); the distance between the test point a and the base station is denoted as d i (p)=‖pq i ‖; Use matrix A to record whether there is any association between the base stations corresponding to the test point a, A tj ∈{0,1}; The average downlink signal-to-noise ratio experienced by a typical user terminal in a slow fading state at the coordinates related to the base station is expressed as

[0011]

[0012] Where p represents the transmission power, Pg′ i (p) represents the channel gain, g i (p) = (4πfd i (p) / v) -α exp(-σ 2 / 2ξ 2 ), f represents the center frequency, v represents the speed of light, α represents the propagation factor, σ represents the shadow standard deviation, ξ=10 / log(10), g′ i (p) represents the power normalized interference experienced by the UE,

[0013] represents the base station set, N0 represents the two-way noise power spectrum density, and W represents the bandwidth;

[0014] For the communication target, the rate of the tth test point can be expressed as:

[0015]

[0016] where g tj (p) = g j (p t ), g′ tj (p) = ∑ n∈J\{j} g tn x nrepresents the normalized interference experienced by a typical user terminal at test point a for a given deployment decision vector.

[0017] Further, in step S2, according to the base station deployment model, setting an objective function to determine the deployment vector and the association matrix with the goal of maximizing the total rate includes: evaluating the positioning performance of the base station deployment model by the positioning error lower bound:

[0018] The estimated user device location is defined as Based on the distance vector The covariance matrix satisfies

[0019]

[0020] in Representatives expectations, represent is positive semidefinite, and For a given p and deployed base stations q = (q1,q z ,…q J ) T The Fisher information matrix is ​​defined as follows:

[0021]

[0022] Since the range measurements are assumed to be independent, we have Thus, it can be inferred that

[0023]

[0024] The value of is called the position error bound and represents the lower bound of the mean square error of the distance between the UE position and its corresponding estimate. The PEB of ToA measurement with uniformly distributed measurement bias is expressed as follows:

[0025]

[0026] where v i The expression is as follows:

[0027]

[0028] And d i =d i (p), θ i =θ i (p) is p and q about the horizontal axis i The angle between them, h(y,d), is expressed as follows:

[0029]

[0030] The formula for measuring positioning performance is as follows:

[0031]

[0032] where v t =[v 1t ,v 2t ,…,v Jt ] T , v j (p t )=v jt ,{F t} i,j =v it v jt sin 2 (θ jt -θ it ).

[0033] Further, in step S2, the objective function includes:

[0034]

[0035] Constraints include:

[0036] Constraint 1 means that for the perceptual positioning performance, b t Less than a given threshold

[0037] b t ≤ζ,

[0038] Constraint 2 means that when a test point is associated with a candidate site, the candidate site must have a base station deployed:

[0039] A≤1 T x T ,

[0040] Constraint three states that each test point can be associated with at most one site:

[0041] A1 J ≤1 T ,

[0042] Constraint 4 states that the deployment must be smaller than M:

[0043] x T 1 J ≤M,

[0044] Constraint 5 means that for the deployment vector x and the association matrix A, the values ​​can only be 0 or 1:

[0045] x∈{0,1} J, A∈{0,1} T×J

[0046] Further, in step S3, processing by approximate convex relaxation includes: introducing a new deployment vector constraint and an incidence matrix constraint to relax constraint five to a standard convex problem, and performing approximate convex relaxation by replacing constraint five with the following inequality constraint:

[0047]

[0048] Further, in step S4, the processing by introducing the auxiliary vector includes:

[0049] r t Rewrite as r′ t , the expression is as follows:

[0050]

[0051] By decomposing it into a series of convex problems: First, define a log function minimization problem as Eq.

[0052]

[0053] According to this formula, r′ t By introducing an intermediate vector y to process r′ t Rewrite it as r″ t , the expression is as follows

[0054]

[0055] At this time, the objective function is:

[0056] Further, in step S3, processing by semi-relaxation technique includes:

[0057] Restate constraint 1 as:

[0058]

[0059] Using a known matrix identity, we can convert Expressed as Tr(F t X); at the same time, due to the binary nature of vector x, It can be expressed as Tr(X⊙V t ), where V t is a diagonal matrix whose diagonal elements are v t , then constraint 1: Tr(X⊙V t )≤ζTr(F t X),

[0060] Furthermore, in step S4, the fully convexified objective function and constraints include:

[0061]

[0062] subject to Tr(X☉V t )≤ζTr(F t X),

[0063] A≤1 T diag(X) T ,

[0064] A1 J ≤1 T ,

[0065] Tr(X)≤M,

[0066]

[0067] Furthermore, in step S5, it includes: using the cvx toolbox to solve the fully convex objective function and constraints to complete the base station deployment.

[0068] In addition, the present invention also provides a synaesthesia integrated base station deployment system including:

[0069] Model building module: perform system modeling on base station deployment to obtain a base station deployment model;

[0070] Objective function module: According to the base station deployment model, the positioning performance of the base station deployment model is evaluated, and the objective function and constraints with the goal of maximizing the total rate are set according to the positioning performance of the base station deployment model;

[0071] Convexification module: The objective function and constraints are processed by approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain convex objective functions and constraints;

[0072] Solution module: solves the fully convex objective function and constraints to complete base station deployment.

[0073] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0074] The present invention sets an objective function and constraints with the goal of maximizing the total rate; then processes the objective function and constraints through approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain a convex objective function and constraints; thereby converting a non-convex problem that is relatively difficult to solve into a problem that can be directly solved, thereby obtaining a base station deployment plan that satisfies the maximum total rate and completing the deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1This is a flow chart of a method for deploying a synaesthesia integrated base station according to Embodiment 1;

[0076] Figure 2 This is a schematic diagram of a base station deployment model described in Example 2;

[0077] Figure 3 This is a block diagram of a synaesthesia integrated base station deployment system described in Example 3; DETAILED DESCRIPTION

[0078] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0079] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0080] Embodiment 1:

[0081] This embodiment provides Figure 1 A method for deploying a synaesthesia integrated base station is shown, comprising:

[0082] S1: Perform system modeling on base station deployment to obtain a base station deployment model;

[0083] S2: According to the base station deployment model, the positioning performance of the base station deployment model is evaluated, and the objective function and constraints with the goal of maximizing the total rate are set according to the positioning performance of the base station deployment model;

[0084] S3: The objective function and constraints are processed by approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain convex objective function and constraints;

[0085] S4: Solve the convex objective function and constraints to complete the base station deployment.

[0086] This embodiment sets an objective function and constraints with the goal of maximizing the total rate; then processes the objective function and constraints through approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain a convex objective function and constraints; thereby converting the non-convex problem that is more difficult to solve into a problem that can be solved directly, thereby obtaining a base station deployment plan that satisfies the maximum total rate and completing the deployment.

[0087] Embodiment 2:

[0088] This embodiment further discloses on the basis of the first embodiment:

[0089] In step S1, the base station deployment model is as follows: Figure 2 The figure includes: J candidate sites as candidate locations for base station deployment, and the states of all candidate sites are stored by deploying decision vectors x j ∈{0,1}, the coordinates of the base station are denoted by q i =(xi ,y i ); the coordinates of the test point a are denoted as p = (x, y); the distance between the test point a and the base station is denoted as d i (p)=‖pq i ‖; Use matrix A to record whether there is any association between the base stations corresponding to the test point a, A tj ∈{0,1}; The average downlink signal-to-noise ratio experienced by a typical user terminal in a slow fading state at the coordinates related to the base station is expressed as

[0090]

[0091] Where p represents the transmission power, Pg′ i (p) represents the channel gain, g i (p) = (4πfd i (p) / v) -α exp(-σ 2 / 2ξ 2 ), f represents the center frequency, v represents the speed of light, α represents the propagation factor, σ represents the shadow standard deviation, ξ=10 / log(10), g′ i (p) represents the power normalized interference experienced by the UE,

[0092] represents the base station set, N0 represents the two-way noise power spectrum density, and W represents the bandwidth;

[0093] For the communication target, the rate of the tth test point can be expressed as:

[0094]

[0095] where g tj (p) = g j (p t ), g′ tj (p) = ∑ n∈J\{j} g tn x n represents the normalized interference experienced by a typical user terminal at test point a for a given deployment decision vector.

[0096] In step S2, according to the base station deployment model, setting an objective function to determine the deployment vector and the association matrix with the goal of maximizing the total rate includes: evaluating the positioning performance of the base station deployment model by the positioning error lower bound:

[0097] The estimated user device location is defined as Based on the distance vector The covariance matrix satisfies

[0098]

[0099] in Representatives expectations, represent is positive semidefinite, and For a given p and deployed base stations q = (q1, q2, ...q J ) T The Fisher information matrix is ​​defined as follows:

[0100]

[0101] Since the range measurements are assumed to be independent, we have Thus, it can be inferred that

[0102]

[0103] The value of is called the position error bound and represents the lower bound of the mean square error of the distance between the UE position and its corresponding estimate. The PEB of ToA measurement with uniformly distributed measurement bias is expressed as follows:

[0104]

[0105] where v i The expression is as follows:

[0106]

[0107] And d i =d i (p), θ i =θ i (p) is p and q about the horizontal axis i The angle between them, h(y,d), is expressed as follows:

[0108]

[0109] The formula for measuring positioning performance is as follows:

[0110]

[0111] where v t =[v 1t ,v 2t ,…,v Jt ] T , v j (p t )=v jt ,{F t} i,j =vit v jt sin 2 (θ jt -θ it ).

[0112] In step S2, the objective function includes:

[0113]

[0114] Constraints include:

[0115] Constraint 1 means that for the perceptual positioning performance, b t Less than a given threshold

[0116] b t ≤ζ,

[0117] Constraint 2 means that when a test point is associated with a candidate site, the candidate site must have a base station deployed:

[0118] A≤1 T x T ,

[0119] Constraint three states that each test point can be associated with at most one site:

[0120] A1 J ≤1 T ,

[0121] Constraint 4 states that the deployment must be smaller than M:

[0122] x T 1 J ≤M,

[0123] Constraint 5 means that for the deployment vector x and the association matrix A, the values ​​can only be 0 or 1:

[0124] x∈{0,1} J , A∈{0,1} T×J

[0125] In step S3, processing by approximate convex relaxation includes: introducing a new deployment vector constraint and an incidence matrix constraint to relax constraint five to a standard convex problem, and performing approximate convex relaxation by replacing constraint five with the following inequality constraint:

[0126]

[0127] In step S4, the processing by introducing the auxiliary vector includes:

[0128] r t Rewrite as r′ t, the expression is as follows:

[0129]

[0130] By decomposing it into a series of convex problems: First, define a log function minimization problem as Eq.

[0131]

[0132] According to this formula, r′ t By introducing an intermediate vector y to process r′ t Rewrite it as r″ t , the expression is as follows

[0133]

[0134] At this time, the objective function is:

[0135] In step S3, processing by semi-relaxation technique includes:

[0136] Restate constraint 1 as:

[0137]

[0138] Using a known matrix identity, we can convert Expressed as Tr(F t X); at the same time, due to the binary nature of vector x, It can be expressed as Tr(X⊙V t ), where V t is a diagonal matrix whose diagonal elements are v t , then constraint 1: Tr(X⊙V t )≤ζTr(F t X),

[0139] In step S4, the fully convexified objective function and constraints include:

[0140]

[0141] subject to Tr(X☉V t )≤ζTr(F t X),

[0142] A≤1 T diag(X) T ,

[0143] A1 J ≤1 T ,

[0144] Tr(X)≤M,

[0145]

[0146] In step S5, it includes: using the cvx toolbox to solve the fully convex objective function and constraints to complete the base station deployment.

[0147] This embodiment sets an objective function and constraints with the goal of maximizing the total rate; then processes the objective function and constraints through approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain a convex objective function and constraints; thereby converting the non-convex problem that is more difficult to solve into a problem that can be solved directly, thereby obtaining a base station deployment plan that satisfies the maximum total rate and completing the deployment.

[0148] Embodiment three:

[0149] This embodiment provides Figure 3 The illustrated synaesthesia integrated base station deployment system includes:

[0150] Model building module: perform system modeling on base station deployment to obtain a base station deployment model;

[0151] Objective function module: According to the base station deployment model, the positioning performance of the base station deployment model is evaluated, and the objective function and constraints with the goal of maximizing the total rate are set according to the positioning performance of the base station deployment model;

[0152] Convexification module: The objective function and constraints are processed by approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain convex objective functions and constraints;

[0153] Solution module: solves the fully convex objective function and constraints to complete base station deployment.

[0154] This embodiment sets an objective function and constraints with the goal of maximizing the total rate; then processes the objective function and constraints through approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain a convex objective function and constraints; thereby converting the non-convex problem that is more difficult to solve into a problem that can be solved directly, thereby obtaining a base station deployment plan that satisfies the maximum total rate and completing the deployment.

[0155] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for deploying a synaesthesia integrated base station, characterized in that: include: S1: Perform system modeling on base station deployment to obtain a base station deployment model; S2: According to the base station deployment model, the positioning performance of the base station deployment model is evaluated, and the objective function and constraints with the goal of maximizing the total rate are set according to the positioning performance of the base station deployment model; S3: The objective function and constraints are processed by approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain convex objective function and constraints; S4: Solve the convex objective function and constraints to complete the base station deployment.

2. The method for deploying a synaesthesia integrated base station according to claim 1, characterized in that: In step S1, the base station deployment model includes: J candidate sites as candidate locations for base station deployment, and the states x of all candidate sites are stored by deploying decision vectors j ∈{0,1}, the coordinates of the base station are denoted by q i =(x i ,y i ); the coordinates of the test point a are denoted as p = (x, y); the distance between the test point a and the base station is denoted as d i (p)=‖pq i ‖; Use matrix A to record whether there is any association between the base stations corresponding to the test point a, A tj ∈{0,1}; The average downlink signal-to-noise ratio experienced by a typical user terminal in a slow fading state at the coordinates related to the base station is expressed as Where p represents the transmission power, Pg′ i (p) represents the channel gain, g i (p) = (4πfd i (p) / v) -α exp(-σ 2 / 2ξ 2 ), f represents the center frequency, v represents the speed of light, α represents the propagation factor, σ represents the shadow standard deviation, ξ=10 / log(10), g′ i (p) represents the power normalized interference experienced by the UE, represents the base station set, N0 represents the two-way noise power spectrum density, and W represents the bandwidth; For the communication target, the rate of the mth test point can be expressed as: where g tj (p) = g j (p t ), g′ tj (p) = ∑ n∈J\{j} g tn x n represents the normalized interference experienced by a typical user terminal at test point a for a given deployment decision vector.

3. The method for deploying a synaesthesia integrated base station according to claim 2, characterized in that: In step S2, according to the base station deployment model, setting an objective function to determine the deployment vector and the association matrix with the goal of maximizing the total rate includes: evaluating the positioning performance of the base station deployment model by the positioning error lower bound: The estimated user device location is defined as Based on the distance vector The covariance matrix satisfies in Representatives Expectations, ≥ represents is positive semidefinite, and For a given p and deployed base stations q = (q1, q2, ...q J ) T The Fisher information matrix is ​​defined as follows: Since the range measurements are assumed to be independent, we have Thus, it is inferred The value of is called the position error bound, which represents the lower bound of the mean square error of the distance between the UE position and its corresponding estimate. The PEB of ToA measurement with uniformly distributed measurement bias is expressed as follows: where v i The expression is as follows: And d i =d i (p), θ i =θ i (p) is p and q about the horizontal axis i The angle between them, h(y,d), is expressed as follows: The formula for measuring positioning performance is as follows: where v t =[v 1t , v 2t , …, v Jt T , v j (p t ) = v jt , {F t} i,j = v it v jt sin 2 (θ jt - θ it ).​ 4. The method for deploying a synaesthesia integrated base station according to claim 3, characterized in that: In step S2, the objective function includes: Constraints include: Constraint 1 means that for the perceptual positioning performance, b t Less than a given threshold b t ≤ζ, Constraint 2 means that when a test point is associated with a candidate site, the candidate site must have a base station deployed: Constraint three states that each test point can be associated with at most one site: A1 J ≤1 T ， Constraint 4 states that the deployment must be smaller than M: Constraint 5 means that for the deployment vector x and the association matrix A, the values ​​can only be 0 or 1: x∈{0,1} J ,A∈{0,1} T×J 。 5. The method for deploying a synaesthesia integrated base station according to claim 4, characterized in that: In step S3, processing by approximate convex relaxation includes: introducing a new deployment vector constraint and an incidence matrix constraint to relax constraint five to a standard convex problem, and performing approximate convex relaxation by replacing constraint five with the following inequality constraint: 0 J×1 ≤x≤1 J×1 0 T×J ≤A≤1 T×J 。。 6. The method for deploying a synaesthesia integrated base station according to claim 5, characterized in that: In step S4, the processing by introducing the auxiliary vector includes: r t Rewrite as r′ t , the expression is as follows: By decomposing it into a series of convex problems: First, define a log function minimization problem as Eq. According to this formula, r′ t By introducing an intermediate vector y to process r′ t Rewrite it as r″ t , the expression is as follows At this time, the objective function is:

7. The method for deploying a synaesthesia integrated base station according to claim 5, characterized in that: In step S3, processing by semi-relaxation technique includes: Restate constraint 1 as: Using a known matrix identity, we can convert Expressed as Tr(F t X); at the same time, due to the binary nature of vector x, It can be expressed as Tr(X⊙V t ), where V t is a diagonal matrix whose diagonal elements are v t , then constraint 1: Tr(X⊙V t )≤ζTr(F t X),.

8. The method for deploying a synaesthesia integrated base station according to claim 5, characterized in that: In step S4, the fully convexified objective function and constraints include: subject toTr(X☉V t )≤ζTr(F t X), A1 J ≤1 T ， Tr(X)≤M, 0 J×J ≤X≤1 J×J 0 T×J ≤A≤1 T×J 。 9. The method for deploying a synaesthesia integrated base station according to claim 1, characterized in that: In step S5, it includes: using the cvx toolbox to solve the fully convex objective function and constraints to complete the base station deployment.

10. A synaesthesia integrated base station deployment system, characterized in that: include: Model building module: perform system modeling on base station deployment to obtain a base station deployment model; Objective function module: According to the base station deployment model, the positioning performance of the base station deployment model is evaluated, and the objective function and constraints with the goal of maximizing the total rate are set according to the positioning performance of the base station deployment model; Convexification module: The objective function and constraints are processed by approximate convex relaxation, introduction of auxiliary vectors and semi-relaxation technology to obtain convex objective functions and constraints; Solution module: solves the fully convex objective function and constraints to complete base station deployment.

Images